• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.75-77
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• 2012

In this paper, We proposed that real-time pupil detection using local binarization at each region of eyes in image. In image obtained a single low-resolution web-camera, we detect a region of face using haar-like feature and then detect each region of eyes depending upon the rate of width and height of region of face respectively. In each region of eyes, we detect the pupil after local preprocessing and binarizing. This pupil detection can be variously used for HCI(Human-Computer Interface) systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.167-170
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• 2010

In this paper, we proposed a novel algorithm to detect the center of pupil in frontal view face. This algorithm, at first, extract an eye region from the face image using integral projection function and variance projection function. In an eye region, detect the center of pupil positions using circular hough transform with sobel edge mask. The experimental results show good performance in detecting pupil center from FERET face image.

• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.11-19
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• 2016

The size of a display is large, The form becoming various of that do not apply to previous methods of gaze tracking and if setup gaze-track-camera above display, can solve the problem of size or height of display. However, This method can not use of infrared illumination information of reflected cornea using previous methods. In this paper, Robust pupil detecting method for eye's occlusion, corner point of inner eye and center of pupil, and using the face pose information proposes a method for calculating the simply position of the gaze. In the proposed method, capture the frame for gaze tracking that according to position of person transform camera mode of wide or narrow angle. If detect the face exist in field of view(FOV) in wide mode of camera, transform narrow mode of camera calculating position of face. The frame captured in narrow mode of camera include gaze direction information of person in long distance. The method for calculating the gaze direction consist of face pose estimation and gaze direction calculating step. Face pose estimation is estimated by mapping between feature point of detected face and 3D model. To calculate gaze direction the first, perform ellipse detect using splitting from iris edge information of pupil and if occlusion of pupil, estimate position of pupil with deformable template. Then using center of pupil and corner point of inner eye, face pose information calculate gaze position at display. In the experiment, proposed gaze tracking algorithm in this paper solve the constraints that form of a display, to calculate effectively gaze direction of person in the long distance using single camera, demonstrate in experiments by distance.

• Journal of the Korea Society of Computer and Information
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• v.22 no.2
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• pp.21-27
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• 2017

In this paper, we propose a pupil detection method using PCA(principal component analysis) and Hough transform. To reduce error to detect eyebrows as pupil, eyebrows are detected using projection function in eye region and eye region is set to not include the eyebrows. In the eye region, pupil candidates are detected using rank order filter. False candidates are removed by using symmetry. The pupil candidates are grouped into pairs based on geometric constraints. A similarity measure is obtained for two eye of each pair using PCA and hough transform, we select a pair with the smallest similarity measure as final two pupils. The experiments have been performed for 1000 images of the BioID face database. The results show that it achieves the higher detection rate than existing method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.90-93
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• 2013

This paper presents a multistage adaptive thresholding method and circular Hough transform for pupil detection. Multistage adaptive thresholding is a thresholding method that applies local image statistic within a neighborhood variable and the global thresholds. Therefore, the method can adopt the benefit of local thresholding and prevent an over segmentation at the same time because of the global image information. To detect a pupil, a circular Hough transform is applied to it in which the pupil pattern is considered as a circle shape. The experimental results show the reliability of our proposed method in detecting pupil properly.

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.27-34
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• 2014

In this paper, we propose a pupil detection method using hybrid projection function and rank order filter. To reduce error to detect eyebrows as pupil, eyebrows are detected using hybrid projection function in face region and eye region is set to not include the eyebrows. In the eye region, potential pupil candidates are detected using rank order filter and then the positions of pupil candidates are corrected. The pupil candidates are grouped into pairs based on geometric constraints. A similarity measure is obtained for two eye of each pair using template matching, we select a pair with the smallest similarity measure as final two pupils. The experiments have been performed for 700 images of the BioID face database. The pupil detection rate is 92.4% and the proposed method improves about 21.5% over the existing method..

• International Journal of Contents
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• v.2 no.3
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• pp.22-26
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• 2006

In this paper we introduce a new circle detection algorithm for occluded on/off pupil and iris boundary extraction. The proposed algorithm employs 7-step processing to detect a center and radius of occluded on/off eye images using the property of the chords. The algorithm deals with two types of occluded pupil and iris boundary information; one is composed of circle-shaped, incomplete objects, which is called occluded on iris images and the other type consists of arc objects in which circular information has partially disappeared, called occluded off iris images. This method shows that the center and radius of iris boundary can be detected from as little as one-third of the occluded on/off iris information image. It is also shown that the proposed algorithm computed the center and radius of the incomplete iris boundary information which has partially occluded and disappeared. Experimental results on RGB images and IR images show that the proposed method has encouraging performance of boundary detection for pupil and iris segmentation. The experimental results show satisfactorily the detection of circle from incomplete circle shape information which is occluded as well as the detection of pupil/iris boundary circle of the occluded on/off image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.828-831
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• 2005

In this paper we propose a method to detect human faces from color image using pupil-template matching. Face detection is done by three stages. (i)separating skin regions from non-skin regions; (ii)generating a face regions by application of the best-fit ellipse; (iii)detecting face by pupil-template. Detecting skin regions is based on a skin color model. we generate a gray scale image from original image by the skin model. The gray scale image is segmented to separated skin regions from non-skin regions. Face region is generated by application of the best-fit ellipse is computed on the base of moments. Generated face regions are matched by pupil-template. And we detection face.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.89-96
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• 2009

A lot of information is contained in an image or a movie rather than in a text, and it is very important thing to extract context from them. In this study, we propose a method to detect an open/closed eye and determine the location of a pupil in an eye image which is extracted from a movie. The image is normalized using transformation into bipolarization with white and black color and horizontalizing, and we measure width and height of an eye. With these information, we can determine the open or closed eye and the location of the pupil. Experiments were done with 52 images of eyes from movies using this method, and we get good results with 98% of correctness in detection of open/closed eyes and 95% in detection of pupil's location.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.215-220
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• 2013

They are well-known geometric properties of a circle that the perpendicular bisector of a chord passes through the center of a circle, and the intersection of the perpendicular bisectors of any two chords is its center. This paper is related to a fast pupil detection method capable of detecting the center and the radius of a pupil using these geometric properties at high speed when detecting the pupil region for iris segmentation. The proposed method is characterized as rapidly detecting the center and the radius of the pupil, extracting the candidate points of the circle in human eye images using morphological operations, and finding two chords using four points on the circular edge, and taking the intersection of the perpendicular bisectors of these two chords for its center. The proposed method can not only detect the center and the radius of a pupil rapidly but also find partially occluded pupils in human eye images.